In　recent　years,　the　stereo　matching　method　based　on　the　convolutional　neural　network　has　been　greatly　developed　and　achieved　accurate　disparity　estimation　results.　However,　the　high　precision　stereo　disparity　estimation　method　is　often　slow　in　reasoning,　so　it　cannot　meet　the　requirements　of　real-time　scene.　Moreover,　this　type　of　method　has　a　large　number　of　parameters　and　is　therefore　not　friendly　to　resource-constrained　embedded　devices.　In　this　paper,　a　novel　2D　cost　aggregation　module　based　on　deformable　convolution　is　constructed　based　on　AnyNet,　and　a　lightweight　stereo　matching　network　named　DSN　is　constructed　accordingly.　We　measured　performance　on　SceneFlow　and　KTTTI2015　dataset.　Experiments　show　that　our　method　achieves　the　approximate　inference　time　and　parameter　quantity　of　AnyNet,　and　the　accuracy　of　disparity　estimation　is　much　better　than　AnyNet,　achieving　a　better　tradeoff　between　performance　and　time.